Self-learning method for keyword based human machine interaction and portable navigation device

ABSTRACT

A self-learning method for keyword based human machine interaction is disclosed. At least one of a plurality of keywords predetermined in a database is selected and a priority score of the selected keyword is updated and a weighted factor is generated for the selected keyword. A weighted score and the weighted factor of the selected keyword are transmitted to the keywords related to the selected keyword. The selected keyword is pushed to a keyword buffer and linkage strengths between the keywords in the keyword buffer are strengthened. When a keyword has been stored in the keyword buffer for over a predetermined reset time period, a reset operation is performed to remove the keyword from the keyword buffer.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority of Taiwan Patent Application No.097138538, filed on Oct. 7, 2008, the entirety of which is incorporatedby reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to human machine interaction, andparticularly to self learning methods for keyword based human machineinteraction.

2. Description of the Related Art

Driven by advancements in Global Positioning System (GPS) chips,modules, and components, lowering price and size thereof, GPS functionshave increasingly been implemented in portable consumer electronicdevices. The GPS can be applied in military, aviation, voyage fields, oreven in mountain-climbing, positioning or car navigation systems. Thecar navigation device is the most popular one among the GPS devices.

Car navigation systems are typically divided into two categories:embedded navigation devices (for example, equipped in a car) or portablenavigation devices. The portable navigation device may be a consumerelectronic product specialized in GPS function or a consumer electronicproduct, such as a personal digital assistant (PDA) or a smart phone)having a navigation system built therein.

Conventionally, the portable navigation device is of simpler design thanthe embedded navigation device, but the GPS functions provided by theportable navigation device are enough for the basic use. Today,navigation devices, used mainly in cars, provide improved positioningfunctions, abundant graphics libraries (for dynamic navigationcapabilities, weather condition forecasts, travel guides, voice soundnavigation capabilities, online graphics library updates and so on) aswell as being integrated with other popular applications.

Some portable navigation devices, used mainly in cars, are equipped withspeech recognition functions. Users can operate the portable navigationdevice without physically touching the controls thereof. For example,users can use voice to operate the portable navigation device to provideinformation built therein or to provide information the device hasretrieved from the network.

However, currently, speech recognition methods do not provideinteractive communication between a user and a portable navigationsystem.

BRIEF SUMMARY OF THE INVENTION

The invention discloses self-learning methods for keyword based humanmachine interaction and a portable navigation device using the same. Theuser can enter a keyword into the portable navigation device so that theportable navigation device may estimate the priority of the keyword andactively or passively provide the user with required information.

The method predetermines a set of keywords in a database of a portablenavigation device, and performs an initialization operation toinitialize the states of the keywords. Then, the method sorts thepriority of the keywords according to the initialization operationresults and displays the keywords on a screen of the portable navigationdevice. Moreover, the method selects at least one of the keywords,assigns a weighted score to the selected keyword, and performs a firstcalculation to refresh a priority score of the selected keyword. Also,the method generates a weighted factor for the selected keyword,transmits the weighted score and the weighted factor of the selectedkeyword to the related keywords (including keywords having arelationship with the selected keywords), and refreshes priority scoresof the related keywords according to the weighted score and the weightedfactor of the selected keyword, and re-sorts the priority of allkeywords accordingly. The keywords on the screen are displayed accordingto the re-sorted result, and the selected keyword is pushed to a keywordbuffer of the portable navigation device, and the length of time thekeyword buffer has stored the keywords therein is monitored. A secondcalculation is performed to strengthen the linkage between the keywordsstored in the keyword buffer and the priority scores of the keywords arerefreshed accordingly. A reset operation is performed to remove thekeyword that has been stored in the keyword buffer for over apredetermined time period, out from the keyword buffer.

An exemplary embodiment of the portable navigation devices of theinvention comprises a database, a speech recognition device, a keywordbuffer, a screen and a microprocessor. The database stores a set ofkeywords and a keyword table. The keyword table stores priority scoresof the keywords and linkage strengths between the keywords. The speechrecognition device receives voice commands. The screen displays thekeywords and is capable of receiving a user input by a stylus or user'sfinger. Next, users may select at least one of the keywords throughvoice commands received by the speech recognition device or the userinput by a stylus or user's finger, and the keyword buffer stores theselected keyword. Following, the microprocessor would execute thefollowing procedures: perform an initialization operation to initializethe states of the keywords, sort the priority of the keywords accordingto the initialization operation results and display the keywords on thescreen, select at least one of the keywords according to the voicecommands received by the speech recognition device or the user input bya stylus or user's finger, assign a weighted score to the selectedkeyword, refresh the keyword table wherein the priority score of theselected keyword is refreshed according to the weighted score of theselected keyword, generate a weighted factor for the selected keyword,transmit the weighted score and the weighted factor of the selectedkeyword to the related keywords, refresh the priority scores of therelated keywords according to the weighted score and the weighted factorof the selected keyword and re-sort the priority of all keywordsaccordingly, display the keywords on the screen according to there-sorted results, push the selected keyword to the keyword buffer,monitor the length of time the keyword buffer has stored the keywordstherein, perform a calculation to strengthen the linkage strengthsbetween the keywords stored in the keyword buffer, refresh the priorityscores of the keywords according to the current linkage strengthsbetween the keywords, and perform a reset operation to remove thekeyword that has been stored in the keyword buffer for over apredetermined time period out from the keyword buffer.

A detailed description is given in the following embodiments withreference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention can be more fully understood by reading thesubsequent detailed description and examples with references made to theaccompanying drawings, wherein:

FIG. 1 depicts a flowchart, which shows an exemplary embodiment of theself-learning methods of the invention;

FIG. 2A depicts an exemplary embodiment of the invention, wherein sevenkeywords are introduced and the relation between the keywords are shownby dashed lines;

FIGS. 2B and 2C show a keyword table at two different time periods;

FIG. 2D shows several keywords of FIG. 2A are selected and pushed into akeyword buffer; and

FIG. 3 depicts the architecture of an exemplary embodiment of theportable navigation device of the invention.

DETAILED DESCRIPTION OF THE INVENTION

The following description shows several exemplary embodiments carryingout the invention. This description is made for the purpose ofillustrating the general principles of the invention and should not betaken in a limiting sense. The scope of the invention is best determinedby reference to the appended claims.

The invention discloses self-learning methods for keyword based humanmachine interaction, and portable navigation devices using the same.

FIG. 1 depicts a flowchart, which shows an exemplary embodiment of aself-learning method for keyword based human machine interaction.

In step S101, at least one set of keywords is predetermined in adatabase of a portable navigation device. FIG. 2A shows one example ofthe set of keywords, wherein the set of keywords includes keywords K1 .. . K7, and the relation between the keywords K1 . . . K7 is shown bydashed lines. Initially, the predetermined keywords may have norelationship or some relationship to each other. As shown in FIG. 2A,there is a link, shown by one dashed line, between two keywords with arelationship. Each keyword is assigned a priority score. The higher thepriority score is, the more frequent the corresponding keyword is used.For example, in a case wherein the top mark is 100 points, keyword K1may be initially assigned 90 points, keyword K2 may be initiallyassigned 85 points and keyword K3 may be initially assigned 83 points .. . and the rest of the keywords K4 . . . K7 are similarly assignedpriority scores. The priority scores of the keywords are stored in akeyword table as shown in FIG. 2B. It should be noted that the number ofkeywords is not limited to 7 and may be other amounts.

In step S102, all keywords predetermined in the database areinitialized, the priority of the keywords is sorted according to theinitialization result, and the keywords are displayed on a screen of theportable navigation device according to the sorted result. In theinitialization operation, the priority of the keywords is sorted basedon the priority scores of the keywords and the linkage strengths betweenthe keywords. Meanwhile, the size and color of the keywords and themaximum number of keywords available to be displayed on the screen aredetermined during the initialization operation. For example, the maximumamount of keywords allowed to be displayed on the screen may be 10; thekeyword with the highest priority score may be shown in red and in thelargest typeface, and the keyword with the second priority score may beshown in blue and in the second largest typeface, and so forth. Thus,users can clearly identify which keyword is most frequently used. Inanother embodiment, when the screen is displaying several keywordsrelating to each other, the keywords may be shown in the similar colors.For example, a keyword “destination” may be represented in green andanother keyword “home” relevant to the keyword “destination” may berepresented in grass green. In some embodiments, the portable navigationdevice is designed to show N keywords at most. The keyword display maybe pages long. In this case, the user can browse through the pages bytouch or voice. For voice commands, the voice commands may be “next” or“next page” and so on.

In step S103, at least one keyword is selected by the user via voicecommands or via other user interfaces such as a touch panel. Forexample, in a case wherein the most frequently triggered event of thekeyword “office” is “the shortest route”, when the user says the keyword“office” or touches the screen to select the keyword “office” orhandwrites the keyword “office” on the touch panel screen, the portablenavigation device may automatically show the shortest route from thecurrent position to the office. In some embodiments, the portablenavigation device may automatically select at least one keywordaccording to trigger of an external event. For example, in a casewherein the user normally goes home from work or a particular restaurantat 6 pm., the portable navigation device may automatically show theroute to go home or the particular restaurant at 6 pm. In anotherembodiment, the portable navigation device may further comprise athermometer to show an external temperature to the user. In anotherembodiment, the portable navigation device may comprise a timer toprovide timely information concerning certain events.

The external event may be a predetermined time period, weathercondition, a position notification, a speed notification, or anavigation mode (for pedestrians or for cars) notification, orcombinations thereof.

In step S104, when at least one keyword is selected, the portablenavigation device may assign a weighted score to the selected keywordand perform a first calculation to refresh the priority score of theselected keyword. For example, when keyword “K1” is selected, theportable navigation device performs the first calculation based on thepriority score of the keyword “K1” and a weighted score assigned to thekeyword “K1” to output a new priority score for the keyword “K1”.Comparing FIGS. 2B and 2C, the priority score of the keyword “K1” isrefreshed, and referring to FIG. 2C, the refreshed priority score ofkeyword “K1” is 95. In step S105, a weighted factor is generated for theselected keyword and the weighted score and the weighted factor of theselected keyword are transmitted to the related keywords of the keyword“K1”, where 0.0<weighted factor≦1.0.

In step S106, the priority scores of the related keywords are refreshedaccording to the weighted score and the weighted factor of the selectedkeyword, and the priority of all keywords are re-sorted accordingly. There-sorting operation is similar to the sorting operation during theinitialization process of step S102, wherein the priority of thekeywords are re-sorted according to the current priority scores of thekeywords and the linkage strengths between the keywords, and the sizeand color of the keywords are determined according to the re-sortedresults. In step S107, the display of the keywords is refreshed on thescreen according to the re-sorted results. In step S108, the selectedkeyword is pushed to a keyword buffer. Step S109 monitors the length oftime the keyword buffer has stored the keywords therein.

In step S110, a second calculation is performed to strengthen thelinkage strengths between the keywords stored in the keyword buffer. Instep S111, according to the linkage strengths refreshed in step S110,the priority scores of the keywords are refreshed. The linkage strengthsmay be labeled from 1 to 10 and determines the values of thecorresponding weighted factors. For example, when keywords “K2” and “K4”are selected after keyword “K1” is selected, two weighted factors aregenerated for the two keywords “K2” and “K4”, wherein the weightedfactor for the keyword “K2” is dependent on the linkage strength betweenthe keywords “K2” and “K1” (8, as shown in FIG. 2B) and the weightedfactor for the keyword “K4” is dependent the linkage strength betweenthe keywords “K4” and “K1” (9, as shown in FIG. 2B). The generated twoweighted factors are used to refresh the priority scores of the keywords“K2” and “K4”, respectively, and furthermore, are used in modifying thelinkage strength between the keywords “K2” and “K1” and the linkagestrength between the keywords “K4” and “K1”. As shown in FIG. 2C, therefreshed value of the linkage strength between the keywords “K2” and“K1” is 9, and the refreshed value of the linkage strength between thekeywords “K4” and “K1” is 9.5. When the keyword “K1” is stored in thekeyword buffer for over a predetermined time period, a reset operationis performed (step S112) to remove the keyword “K1” from the keywordbuffer. The scheme may return to step S103 when another keyword isselected.

FIG. 3 depicts the architecture of an exemplary embodiment of theportable navigation device of the invention.

The portable navigation device 300 comprises a speech recognition device310, a microprocessor 320, a database 330, a keyword buffer 360 and ascreen 370. The database 330 includes a plurality of keywords 340 and akeyword table 350. The screen 360 may be a touch panel display.

The keywords 340 include a set of keywords K1 . . . K7 shown in FIG. 2A.The keyword table 350 may store the data shown in FIG. 2B, whichincludes a column of keywords, a column of priority scores and a columnrecording linkage strengths between the keywords. Initially, thepredetermined keywords have no relationship or some relationship to eachother. There is a link between the keywords having a relationship.Referring to FIG. 2A, the links are represented by dashed lines. Each ofthe keywords is assigned a priority score. High priority scores meanthat the corresponding keywords are frequently used.

After the portable navigation device 300 is turned on, themicroprocessor 320 initializes all keywords in the database 330 andsorts the priority of the keywords and displays the keywords on thescreen 360 according to the initialization result. Duringinitialization, the microprocessor 320 sorts the priority of thekeywords based on the priority scores of the keywords and the linkagestrengths between the keywords. In addition, the microprocessor 320 setsthe size and color of the keywords and the maximum number of keywordsallowed to be displayed on the screen. For example, the maximum amountof keywords allowed to be displayed on the screen may be 10. In someembodiments, the portable navigation device is designed to show Nkeywords at most. The keywords may be displayed on pages. The user canbrowse through the pages by touch panel techniques or by voice commandcontrol techniques. In voice command control techniques, the voicecommands may be “next” or “next page” and so on.

There are many techniques which users may use to input command to theportable navigation device. For example, users may control the device byvoice commands, and the speech recognition device 310 may receive voicecommands from the user input 380, wherein the voice commands may containat least one of the keywords. In another embodiment, the user maycontrol the device via a touch panel display, and the screen 370 maydetect the user input 380 (by a stylus or user's finger), wherein atleast one of the keywords is selected. In some embodiments, the portablenavigation device may comprise a thermometer, to detect an externaltemperature and provide the temperature information to the user. In someembodiments, the portable navigation device may further comprise atimer, to provide timely information concerning certain events.

When at least one of the keywords is selected by the user via the userinput 380, the microprocessor 320 performs the step shown in step S104of FIG. 1, wherein a weighted score is assigned to the selected keywordand a first calculation is performed to refresh the keyword table 350.Thus, refreshing the priority score of the selected keyword. Referringto FIG. 2A, when keyword “K1” is selected, the microprocessor 320performs the first calculation according to the priority score of thekeyword “K1” and the weighted score assigned to the keyword “K1” toobtain a new priority score for the keyword “K1”. Comparing FIGS. 2B and2C, the priority score of the keyword “K1” is refreshed; FIG. 2B showsthat the original priority score of the keyword “K1” is 90 and FIG. 2Cshows that the refreshed priority score of the keyword “K1” is 95. Afterthe first calculation, the microprocessor 320 generates a weightedfactor (0.0<weighted factor≦1.0) for the selected keyword, and transmitsthe weighted score and the weighted factor of the selected keyword tothe related keywords.

The microprocessor 320 refreshes the keyword table 350 according to theweighted score and weighted factor of the selected keyword, wherein thepriority scores of the related keywords of the keyword “K1” arerefreshed. According to the refreshed priority scores, themicroprocessor 320 further re-sorts the priority of the keywords. There-sorting operation is similar to the sorting process of theinitialization operation, wherein the size and color of the keywords aredetermined according to the priority scores of the keywords and thelinkage strengths between the keywords. The microprocessor 320 displaysthe keywords on the screen 370 according to the re-sorted results, andpushes the selected keyword to the keyword buffer 360. Themicroprocessor 320 further monitors the length of time the keywordbuffer 360 has stored the keywords therein.

After storing the selected keyword in the keyword buffer 360, themicroprocessor 320 performs a second calculation to strengthen thelinkage strengths between the keywords stored in the keyword buffer 360and refreshes priority scores of all keywords recorded in the keywordtable 350 based on the current linkage strengths between the keywords.The linkage strengths may be labeled from 1 to 10 and determines thevalues of the corresponding weighted factors. For example, when keywords“K2” and “K4” are selected after keyword “K1” is selected, two weightedfactors are generated for the two keywords “K2” and “K4”, wherein theweighted factor for the keyword “K2” is dependent on the linkagestrength between the keywords “K2” and “K1” (8, as shown in FIG. 2B) andthe weighted factor for the keyword “K4” is dependent the linkagestrength between the keywords “K4” and “K1” (9, as shown in FIG. 2B).The generated two weighted factors are used in refreshing the priorityscores of the keywords “K2” and “K4”, respectively, and furthermore, areused in modifying the linkage strength between the keywords “K2” and“K1” and the linkage strength between the keywords “K4” and “K1”. Asshown in FIG. 2C, the refreshed value of the linkage strength betweenthe keywords “K2” and “K1” is 9, and the refreshed value of the linkagestrength between the keywords “K4” and “K1” is 9.5. When the keyword“K1” is stored in the keyword buffer for over a predetermined timeperiod, the microprocessor 320 performs a reset operation to remove thekeyword “K1” from the keyword buffer.

As shown in the aforementioned paragraphs, when a keyword is selected,the priority score of the selected keyword is increased, that isdependent on the strength of linkage between the selected keyword andits related keywords and the weighted factor generated for the selectedkeyword. In addition, the linkage strengths between the newly selectedkeywords are accordingly strengthened thereafter. Thus, the priorityscore of the selected keyword is in positive correlation with thelinkage strengths between the newly selected keywords and weightedfactor of the selected keyword, and the linkage strengths between thenewly selected keywords are in positive correlation with the priorityscore of the selected keyword.

In some embodiments, the monitoring procedure may allow all keywords tobe stored in the keyword buffer for an identical time limit. In otherembodiments, the time limits for different keywords are different. Thetime limit for each keyword may be determined according to thesignificance of the keyword.

The self-learning method for the keyword based human machine interactionis an intelligent learning method. When a keyword is selected, thepriority score of the selected keyword is increased and is dependent onthe linkage strengths between the selected keyword and the relatedkeywords and the weighted factor generated for the selected keyword. Inaddition, the linkage strengths between the newly selected keywords areaccordingly strengthened thereafter. The self-learning method allows theportable navigation device to automatically show the frequently usedinformation on a screen according to human machine interaction or anexternal event, which is more convenient for users.

The invention further discloses implementing storage media (such as anoptical disc, a floppy disc, or a removable hard disc) to recordcomputer readable right permission programs which realizes theaforementioned self-learning methods for keyword based human machineinteraction. The program is basically composed of several codes, such ascode segments for architecture building, code segments for permissiontables, code segments for system settings, and code segments for programallocation. The code segments may realize the aforementioned steps orfunctions.

While the invention has been described by way of example and in terms ofthe preferred embodiments, it is to be understood that the invention isnot limited to the disclosed embodiments. To the contrary, it isintended to cover various modifications and similar arrangements (aswould be apparent to those skilled in the art). Therefore, the scope ofthe appended claims should be accorded the broadest interpretation so asto encompass all such modifications and similar arrangements.

What is claimed is:
 1. A self-learning method for keyword based humanmachine interaction, comprising: predetermining a set of keywords in adatabase of a portable navigation device; performing an initializationoperation to initialize the set of keywords; sorting and displaying theset of keywords on a screen of the portable navigation device accordingto results of the initialization operation; selecting at least one ofthe keywords; assigning a weighted score to the selected keyword andperforming a first calculation to refresh a priority score of theselected keyword; generating a weighted factor for the selected keywordand transmitting the weighted score and the weighted factor of theselected keyword to the keywords related to the selected keyword;refreshing, according to the weighted score and the weighted factor ofthe selected keyword, priority scores of the keywords related to theselected keyword and accordingly re-sorting all keywords; displaying thekeywords on the screen according to results of the re-sorting; pushingthe selected keyword to a keyword buffer of the portable navigationdevice; performing a second calculation to strengthen linkage strengthsbetween the keywords stored in the keyword buffer; refreshing priorityscores of all keywords according to the linkage strengths; andperforming a reset operation to remove the selected keyword out of thekeyword buffer when the selected keyword stored in the keyword bufferhas been stored over a predetermined time period.
 2. The method asclaimed in claim 1, wherein the keywords initially have no relationshipor some relationship to each other and, for any pair of keywords havinga relationship, there is a link representing the linkage strengththerebetween.
 3. The method as claimed in claim 1, wherein the higherthe priority score is, the more frequent the corresponding keyword isused.
 4. The method as claimed in claim 1, wherein the priority scoresof the keywords are stored in a keyword table.
 5. The method as claimedin claim 1, wherein the initialization operation comprises: sorting thepriority of the keywords according to the priority scores of thekeywords and the linkage strengths between the keywords; and setting thesize and color of the keywords.
 6. The method as claimed in claim 5,wherein the keywords having a relationship are displayed in similarcolors.
 7. The method as claimed in claim 1, wherein the priority scoreof the selected keyword is in positive correlation with the linkagestrengths between the newly selected keywords and the weighted factor ofthe selected keyword.
 8. The method as claimed in claim 1, wherein thelinkage strengths between the newly selected keywords are in positivecorrelation with the priority score of the selected keyword.
 9. Themethod as claimed in claim 1, wherein the step of keyword selection isimplemented by speech recognition techniques or by user input via otherinterfaces.
 10. The method as claimed in claim 1, wherein the keywordselection is triggered by an external event.
 11. The method as claimedin claim 10, wherein the external event is a predetermined time period,weather condition, a position notification, a speed notification, or anavigation mode (for pedestrians or for cars) notification, orcombinations thereof.
 12. The method as claimed in claim 1, wherein thekeyword selection is based on a detected result of a detector.
 13. Themethod as claimed in claim 1, wherein the step of refreshing a priorityscore further comprises refreshing the linkage strengths between thekeywords related to the selected keyword.
 14. A portable navigationdevice, comprising: a database, comprising a set of keywords and akeyword table, wherein the keyword table records priority scores of thekeywords and linkage strengths between the keywords; a speechrecognition device, receiving voice commands; a screen, displaying thekeywords and receiving user input triggered by a stylus or user'sfinger; a keyword buffer, storing the keyword selected by the voicecommands or the user input triggered by a stylus or user's finger; amicroprocessor, performing an initialization operation to initialize theset of keywords, sorting priority of the set of keywords according toresults of the initialization operation and displaying the set ofkeywords on the screen according to results of the sorting, selecting atleast one of the keywords according to the voice commands or the userinput triggered by a stylus or user's finger, assigning a weighted scoreto the selected keyword, refreshing the keyword table wherein the prioryscore of the selected keyword is refreshed according to the weightedscore of the selected keyword, generating a weighted factor for theselected keyword, transmitting the weighted score and the weightedfactor of the selected keyword to the keywords related to the selectedkeyword, refreshing the priority scores of the keywords related to theselected keyword according to the weighted score and the weighted factorof the selected keyword and re-sorting the priority of the keywordsaccordingly, displaying the keywords on the screen according to resultsof the re-sorting, pushing the selected keyword to the keyword buffer,strengthening linkage strengths between the keywords stored in thekeyword buffer and refreshing the priority scores of all keywordsaccordingly, and performing a reset operation to remove the selectedkeyword out of the keyword buffer while the selected keyword has beenstored in the keyword buffer over a predetermined time period.
 15. Theportable navigation device as claimed in claim 14, wherein the keywordsinitially have no relationship or some relationship to each other, andfor any pair of keywords having a relationship, there is a linkrepresenting the linkage strength therebetween.
 16. The portablenavigation device as claimed in claim 14, wherein the higher thepriority score is, the more frequent the corresponding keyword is used.17. The portable navigation device as claimed in claim 14, wherein whenperforming the initialization operation, the microprocessor sorts thepriority of the keywords according to the priority scores of thekeywords and the linkage strengths between the keywords, and sets thesize and color of the keywords.
 18. The portable navigation device asclaimed in claim 17, wherein the keywords having a relationship aredisplayed in similar colors.
 19. The portable navigation device asclaimed in claim 14, wherein the priority score of the selected keywordis in positive correlation with the linkage strengths, between the newlyselected keywords and the weighted factor of the selected keyword. 20.The portable navigation device as claimed in claim 14, wherein thelinkage strengths between the newly selected keyword are in positivecorrelation with the priority score of the selected keyword.
 21. Theportable navigation device as claimed in claim 14, wherein themicroprocessor selects at least one of the set of keywords based on thevoice commands received by the speech recognition device or user inputvia other interfaces.
 22. The portable navigation device as claimed inclaim 14, wherein the microprocessor selects at least one of the set ofkeywords based on an external event.
 23. The portable navigation deviceas claimed in claim 22, wherein the external event is a predeterminedtime period, weather condition, a position notification, a speednotification, or a navigation mode (for pedestrians or for cars)notification, or combinations thereof.
 24. The portable navigationdevice as claimed in claim 14, wherein the microprocessor selects atleast one of the set of keywords based on a detected result of adetector.
 25. The portable navigation device as claimed in claim 14,wherein when refreshing the priority scores of the keywords, themicroprocessor further refreshes linkage strengths between the keywords.